Search Results (527)

Soil salinization severely threatens agricultural productivity and ecosystem sustainability, particularly in coastal regions. Halophyte-based phytoremediation is a promising strategy, yet how rhizosphere soil legacy effects at different restoration ages influence subsequent plant growth and microbial communities remains poorly understood. Here, rhizosphere soils of Nitraria tangutorum at 1- (BC-1), 2- (BC-2), and 3-year (BC-3) restoration stages and non-rhizosphere bulk soil (CK) were sampled, with alfalfa cultivated as a bioindicator to assess soil physicochemical properties, plant growth, stress physiology, and rhizosphere microbiota. With increasing restoration age, rhizosphere soil shifted from a state of salt accumulation and nutrient deficiency to one of salt depletion and nutrient enrichment, with BC-3 exhibiting the highest soil organic matter, total phosphorus, and alkali-hydrolyzable nitrogen and the lowest total salt and soluble Na⁺. Alfalfa growth was suppressed in BC-1 and BC-2 soils, but significantly promoted in BC-3, accompanied by the lowest malondialdehyde and proline content, indicating effective alleviation of oxidative and osmotic stress. Microbial diversity peaked at BC-2, whereas the total proportion of halotolerant bacteria declined from 0.44 (BC-1) to 0.34 in BC-3 (significantly lower than CK), suggesting a successional shift from a stress-dominated community toward a functionally specialized consortium. Regression analyses identified soluble sodium as the variable most strongly associated with growth inhibition (R² > 0.80) for plant height and root length. We suggest soluble sodium may represent the principal factor associated with growth inhibition and that a positive-feedback loop among plant Na⁺ sequestration, microbial carbon sequestration, and soil maturation may sustain long-term saline–alkali soil improvement. These findings suggest a three-stage successional mechanism and highlight the critical role of restoration age in mediating plant–microbe–soil synergistic remediation of coastal saline soils. Full article

Global climate change has posed a serious threat to agricultural production and food security. Crop growth models, with their excellent simulation and prediction capabilities, have become one of the important tools for guiding agricultural production and ensuring food security. This study provides a review of the research progress in crop growth models. The development history of the models is summarized into four stages, including process modeling, system simulation, model application, and algorithm expansion. According to different driving factors, the crop growth models can be categorized into solar radiation-driven, soil moisture content-driven, meteorological factor-driven, and integrated factor-driven models. In terms of the countries of development, the models mainly include those from the Netherlands, the United States, Australia, and China. Regarding applications, crop growth models are primarily applicable to adaptability assessment, agricultural resource and crop cultivation management, and climate change evaluation. Since their initial development, these models have enhanced their mechanistic nature through various approaches, such as incorporating surface mulching modules, considering the response of root water uptake to soil salt stress, and preliminarily introducing physiological regulation processes. By integrating with remote sensing technology, the spatial scale of the models has been expanded from the point scale to the regional or even global scale, and the accuracy of regional-scale yield estimation has been significantly improved through assimilation with remote sensing. Meanwhile, crop growth models have also been combined with intelligent algorithms to optimize irrigation scheduling, and to perform model parameter optimization. Looking forward, potential future research directions of crop growth models include extending the soil submodule from one-dimensional to two/three-dimensional water–heat–solute transport, moving toward a more mechanistic crop growth modeling, integration with remote sensing, and incorporating artificial intelligence. This study serves as a reference for further development and application of crop growth models, and provides technical support for the development of sustainable agriculture. Full article

High-resolution Normalized Difference Vegetation Index (NDVI) data are essential for monitoring fine-scale coastal environmental dynamics, yet persistent cloud cover, rapid geomorphic change, and strong spatial heterogeneity limit the availability of temporally continuous observations. Existing spatiotemporal fusion approaches can partially address these limitations, but many rely primarily on data-driven feature learning and do not explicitly incorporate geographic information, leading to boundary blurring, structural inconsistency, and sensitivity to background noise in complex coastal environments. This study presents a geographically constrained Transformer-based framework for 2 m NDVI spatiotemporal reconstruction in coastal landscapes named Coastal-Prior-Embedded Global–Local Fusion Transformer (Coastal-GLFT). The approach integrates high-resolution Gaofen-6 panchromatic and multispectral imagery with high-frequency wide-field-view observations and auxiliary geographic datasets describing elevation, coastline proximity, and land use/land cover. Geographic priors were incorporated as explicit spatial constraints, while a spatiotemporal gating mechanism and global–local fusion architecture were used to improve the representation of temporal variation and multi-scale spatial structure. The method was evaluated using a multi-temporal dataset for the Yellow River Delta comprising 49 high-resolution scenes and 137 coarse-resolution scenes acquired between 2020 and 2025. Compared with representative physics-based, convolutional neural network, generative adversarial network, and Transformer-based fusion methods, the proposed approach reduced reconstruction error by approximately 5–72%, increased signal fidelity by approximately 1–12%, and improved structural similarity by approximately 2–52%. Compared with the strongest Transformer-based baseline, SwinSTFM, Coastal-GLFT reduced RMSE from 0.0896 to 0.0855, increased PSNR from 36.19 dB to 37.09 dB, and improved SSIM from 0.8551 to 0.8742. Qualitative analysis further demonstrated improved preservation of boundary structure, spatial continuity, and heterogeneous coastal features, including aquaculture ponds, tidal creeks, and fragmented wetlands. These results indicate that integrating geographic constraints with multi-scale Transformer-based reconstruction can improve the fidelity and structural consistency of high-resolution NDVI reconstruction in complex coastal environments. The framework provides a basis for fine-scale coastal vegetation monitoring and land-cover analysis, while future work should assess transferability across diverse coastal systems and improve computational scalability. Full article

In coastal wetlands, Tamarix chinensis often occurs as patches intermixed with Phragmites australis, Suaeda salsa, and saline–alkaline bare soil. This mixed distribution makes tamarisk prone to omission in medium-resolution remote sensing classification, while the recall of the target species is often masked by a relatively high overall accuracy. In this study, we focused on the Yellow River Delta National Nature Reserve and developed a multi-source feature set using summer 2025 Sentinel-2, Sentinel-1, and UAV/GPS data, comprising spectral, SAR, phenological, and red-edge-oriented features. To enhance the separability between tamarisk and co-occurring herbaceous vegetation, we introduced a red-edge–vegetation-cover coupling feature (RE_cov) based on their contrasting responses in the red-edge region. Within an XGBoost framework, we evaluated the marginal contribution of this feature using feature ablation, replacement, and spatial block cross-validation. The full feature set achieved an AUC of 0.8042, a recall of 0.9340, and an overall accuracy of 0.8194 on an independent test set. Ablation and replacement experiments showed that the red-edge-oriented features contributed to both model separability and tamarisk recall, and this contribution remained evident under spatial block validation. We further converted the pixel-level extraction results into local tamarisk density grades, revealing a pattern of a few clustered cores embedded within a broad low-density background. These results suggest that target-species-oriented red-edge–vegetation-cover coupling features can improve tamarisk recall while maintaining acceptable overall accuracy, providing a spatial product to support zoned patrol and management in protected coastal wetlands. Full article

Coastal saline–alkaline land has considerable potential for soil carbon sequestration, but how different land-use types affect microbial resource limitation and carbon-use efficiency (CUE) in coastal saline–alkaline soils remains unclear. Four representative land-use types, namely bare land (BL), wetland (WL), grassland (GL), and forest land (FL), were investigated in the coastal saline–alkaline soils of the Yellow River Delta. Soil physicochemical properties, microbial biomass, and extracellular enzyme activities were measured, and ecoenzymatic stoichiometry, microbial resource limitation, and CUE were subsequently calculated. Compared with BL, vegetated land-use types decreased electrical conductivity by 52.1–95.8%, while soil water content, soil nutrient indicators, and microbial biomass indicators increased by 47.1–77.5%, 2.6–136.8%, and 2.2–274.4%, respectively. WL was mainly phosphorus-limited, whereas BL, GL, and FL were primarily nitrogen-limited. Despite relatively high soil organic carbon and nutrient availability, GL showed the strongest N limitation and was the only land-use type showing C limitation. Model-estimated CUE ranged from 0.544 to 0.579 and followed the order FL > BL > WL > GL. Random forest analysis showed that soil physicochemical properties contributed most to CUE variation (42.9%). Structural equation modeling further indicated that soil physicochemical properties were indirectly associated with CUE, mainly through stoichiometric characteristics and microbial resource limitation, showing positive and negative associations, respectively. These findings provide microbial evidence for optimizing land-use patterns, vegetation restoration, and carbon-oriented ecological restoration in coastal saline–alkaline land. Full article

Nutrient stoichiometry provides a powerful framework for linking nutrient limitation to plant community biomass, especially in grasslands undergoing degradation in the agro-pastoral ecotone, where nitrogen (N) fertilization and mowing have become two widespread key management practices. However, their influence on nutrient stoichiometry has received little attention, especially beyond the leaf carbon (C):N:phosphorus (P) ratio. Here, we conducted a field experiment on the Mongolian Plateau wherein we quantified 19 nutrient ratios for soils and three plant components (aboveground plants, litter, and belowground roots) following 12 years of N addition (0, 2, and 10 g N m⁻² year⁻¹) combined with mowing, and grouped these ratios into four sets with C, N, P, and potassium (K) as the numerators. Under N addition, nutrient stoichiometry in plant components and soils changed markedly, whereas mowing management resulted in negligible changes in most C-, N-, and K-based nutrient ratios. Furthermore, mowing and N addition interactively and significantly affect P-based nutrient ratios. The responses of nutrient stoichiometry differed among plant components and soils, and also depended on the level of N input, and these ratios with C and N as numerators generally showed greater variability than those with P and K in the plant–soil system. Plant community biomass was associated with nutrient ratios in both plant components and in soils, although the relationships were not always significant. Long-term N addition resulted in rate-dependent shifts in nutrient stoichiometry, whereas mowing had only weak modifying effects. Extending nutrient stoichiometry framework (including neglected ratios, e.g., N:K) beyond leaf C:N:P to encompass entire plant–soil systems can help local government and ranch owners manage grasslands more cost-effectively because of simple assessment procedures, and could further provide more comprehensive insights into nutrient limitation and main hypotheses of ecological stoichiometry in grassland ecosystems. Full article

Ground-penetrating radar (GPR) was utilized for subsurface soil investigation in the Yellow River Delta, aiming to provide a scientific basis for the remediation performance of saline soils. The study particularly focuses on the red clay layer, a typical and characteristic soil horizon in this region. GPR antennas with central frequencies of 400 MHz and 900 MHz were adopted to investigate shallow soils within 1 m of the ground surface across three experimental plots (pits, undisturbed soils, and tilled soils) and 18 scattered measurement sites, followed by systematic analysis and interpretation of the acquired GPR profiles. During data acquisition, reasonable survey lines were deployed across the patchy bare areas of cultivated lands covering the experimental plots and measurement points to collect raw GPR data. Meanwhile, subsurface soil data were collected via test pits and borehole sampling along the survey lines. Raw GPR data were further preprocessed and postprocessed to characterize soil horizons and interpret subsurface stratigraphic structures. Finally, the correlations between the relative dielectric permittivity, reflection coefficient, and reflected wave amplitude of each soil layer were systematically analyzed. The results demonstrate that the 400 MHz antenna enables effective identification of soil layers within 1 m depth, while the 900 MHz antenna provides high-resolution detection for soil layers above 0.5 m. The red clay layer presents a distinct strong-amplitude reflection on GPR profiles, and the average relative dielectric permittivity of soils across the study area reaches 30.57. GPR profiles reveal that soil horizons with an absolute reflection coefficient greater than 0.01 yield detectable continuous reflection signals and allow uninterrupted stratigraphic interpretation. An empirical formula was established to calculate soil relative dielectric permittivity from soil moisture content, with a correlation coefficient of 0.9173. However, this formula ignores the influences of soil salinity and other trace soil elements. This study realizes rapid and accurate characterization of the depth and thickness of shallow soil layers, providing technical support for soil remediation of saline–alkali land in the Yellow River Delta. The findings also provide a valuable reference for evaluating the remediation effects, optimizing arable land utilization, preventing and mitigating soil salinization risks, and promoting the sustainable economic development of the study area. Full article

Surface dust samples were collected from bus stops and parking lots in different functional areas of Binzhou City, Shandong Province, China. This study investigated the contamination characteristics, source apportionment, and potential ecological and health risks of potentially toxic elements (PTEs) in these dust samples. Eight target PTEs, including As, Zn, Pb, Cu, Cd, Cr, Ni, and Mn, were quantitatively analyzed. The results revealed distinct concentration differences in these elements between bus stop dust and parking lot dust. Several PTEs exceeded the corresponding local soil background values, predominantly Zn, Pb, Cu, Cd and Cr. Principal component analysis (PCA) indicated that Zn, Pb, Cu, Cr, Ni, and Mn in bus stop dust were mainly sourced from traffic emissions, whereas As and Cd primarily originated from atmospheric deposition. For parking lot dust, Zn, Pb, Cu, Cd, Cr, and Mn were predominantly attributed to traffic sources, while As and Ni were mainly derived from natural background sources. The geo-accumulation index (I_geo) demonstrated that As, Cr, Ni, and Mn had negligible environmental impact, Pb, Cu, and Cd induced slight pollution, and Zn resulted in moderate pollution. Except for Cd, the average individual potential ecological risk index ($E_r^i$) values for all elements were below 40, suggesting a low ecological risk. Cd posed a moderate ecological hazard, whereas the comprehensive ecological risk index ($E_r^i$) values of all analyzed elements were at an extremely low level. The hazard index (HI) values via different exposure pathways and for all PTEs in both bus stops and parking lots were lower than 1, indicating no significant non-carcinogenic health risk. The carcinogenic risk ranking of elements was Cr > Cd > Ni > As, and their carcinogenic risk values (CR) via inhalation exposure were below 1 × 10⁻⁶, indicating no carcinogenic risk. This study provides a scientific basis for the environmental quality control and risk management of surface dust in urban bus stops and parking lots. Full article

Tire wear particles (TWPs) are a significant source of microplastics and chemical additives in the environment; however, differences in the toxicity of particles from different vehicle types remain unclear. This hydroponic study compared the phytotoxicity of leachates from aircraft- and automobile-derived TWPs on mung bean. Both leachates inhibited seed germination and seedling growth, with aircraft TWP leachates showing stronger effects, including greater germination delays and more pronounced reductions in shoot height, root length, and root surface area. Physiological analyses revealed that TWP leachates induced oxidative stress, characterized by significant suppression of superoxide dismutase (SOD) activity, compensatory increases in catalase (CAT) and peroxidase (POD) activities, and marked accumulation of malondialdehyde (MDA), indicating severe membrane lipid peroxidation. Chlorophyll content decreased in all groups, with greater reductions under aircraft leachates. Toxicological Priority Index (ToxPi) modeling identified zinc as the shared primary risk factor, while aircraft tire-specific additives (e.g., dicyclohexylamine, 1,2-dihydro-2,2,4-trimethylquinoline) constituted a distinct risk component linked to differentiated formulations. Aircraft TWP leachates thus exhibit stronger phytotoxicity through multiple pathways. These findings support refined environmental risk assessment and targeted control measures for aircraft TWPs. Full article

Amid tightening ecological constraints, accelerating urbanization transition, and increasingly complex spatial governance, the coordinated evolution of Urban Green Space (UGS) and New-Type Urbanization (NTU) has become central to green transition and high-quality development in major river basins. Drawing on city-level panel data for the Yangtze River Economic Belt (YREB) and the Yellow River Basin (YRB) from 2006 to 2022, this study integrates a Coupling Coordination Degree (CCD) model, spatial statistical analysis, and interpretable machine learning to investigate UGS-NTU coordination, with emphasis on spatiotemporal evolution, spatial differentiation, and nonlinear contribution pathways. The findings indicate that: (1) UGS and NTU levels rise in both basins, but their spatial trajectories differ substantially. The YREB exhibits river-oriented expansion and gradient diffusion, whereas the YRB features nodal agglomeration and discontinuous expansion. (2) The CCD improves overall in both basins, with downstream areas leading, the middle reaches following, and the upper reaches lagging behind; UGS lag is widespread in the middle and upper reaches. (3) The YRB shows stronger spatial agglomeration, more pronounced regional differentiation, and more persistent low-value clustering, while the YREB is characterized by stable high-value clustering in the Yangtze River Delta. (4) The YREB is mainly associated with green space system optimization, whereas the YRB is more closely associated with improvements in the foundational capacities of NTU. Both associations exhibit clear nonlinear characteristics. This study provides empirical support for differentiated green transition and high-quality development strategies in major river basins. Full article

The Yellow River Delta is a representative coastal saline–alkaline ecosystem in China, where high salinity and complex soil properties create a distinct habitat that significantly shapes microbial community structure and function. In this study, we analyzed 34 saline–alkaline soil samples from four regions within the delta. We characterized soil physicochemical properties (salt content, electrical conductivity, and pH) and systematically assessed fungal diversity, potential ecological functions, and their relationships with environmental variables using both internal transcribed spacer high-throughput sequencing and culture-based isolation. Sequencing generated 1,137,196 sequences that clustered into 13,574 operational taxonomic units (OTUs), with Good’s coverage values ranging from 0.96 to 1.00, indicating sufficient sequencing depth. The soils were generally alkaline and exhibited pronounced spatial heterogeneity in salinity and electrical conductivity. Sequencing analyses revealed Ascomycota and Basidiomycota as the dominant fungal phyla. Alpha diversity tended to decline with increasing salt content and electrical conductivity; however, substantial within-group variability indicated strong microenvironmental influences. Beta diversity analyses revealed distinct clustering patterns in community structure among regions based on PCoA ordinations. Redundancy analysis revealed that soil pH had the only significant unique contribution to fungal community variation. However, all three measured edaphic factors together explained only 17% of the total community variation. Functional inference using the FUNGuild database identified diverse fungal trophic modes and several plant-associated taxa in several samples. Culture-based approaches yielded 347 isolates representing 52 genera. Among the isolates, the vast majority (>95%) belonged to Ascomycota, with Basidiomycota represented by only a few isolates, which is consistent with the dominance of Ascomycota observed in the high-throughput sequencing data. Comparisons between sequencing and cultivation results demonstrated complementary diversity profiles and highlighted a substantial reservoir of nonculturable fungi in these soils. Overall, this study clarifies spatial patterns and key environmental drivers of fungal diversity in the Yellow River Delta and establishes a foundational culture collection for future ecological restoration efforts. Full article

To understand how habitat heterogeneity drives fungal community assembly in different habitats of the pied avocet (Recurvirostra avosetta), we analyzed four habitat types (water bodies, aquatic plants, soil, and nest sediments) using high-throughput sequencing. A total of 9980 ASVs (Amplicon Sequence Variants) were detected, with only 68 shared across all habitats, indicating strong community differentiation. Ascomycota and Basidiomycota dominated (50–60% relative abundance), reflecting fungal adaptability to wetlands. Water bodies showed significantly higher alpha diversity than aquatic plants and nest sediments. Beta diversity and principal coordinates analysis (PCoA) revealed closer similarity in fungal composition between water and aquatic plant communities, whereas soil and nest sediments formed distinct clusters. PERMANOVA based on binary Jaccard distances further confirmed that habitat type explained 10.9% of the variation in fungal community structure (R² = 0.109, p = 0.001). LEfSe (LDA Effect Size) identified habitat-specific indicator taxa, supporting niche filtering and competitive exclusion as selection mechanisms. The co-occurrence network was dominated by positive correlations, suggesting metabolic complementarity that maintains ecosystem stability. Unclassified fungi accounted for 18–22% of communities, representing untapped fungal resources. These findings support that habitat heterogeneity governs multi-media fungal assembly, revealing how microhabitat conditions regulate fungal composition, diversity, and interactions. This study provides a theoretical basis for biodiversity conservation and ecological restoration in avocet habitats. Full article

Accurate discrete element method (DEM) modelling of mature peanut plants is essential for simulating peanut harvesting, pod detachment, and harvest-loss formation. However, existing peanut DEM models are usually simplified as isolated pods, rigid cylindrical particles, or partial stem–pod structures, which limits their ability to represent the flexible deformation of vines and pod stalks and the fracture behaviors at the pod–pod stalk junction. In this study, a DEM-based flexible plant model was developed for mature peanut plants. The geometric dimensions, contact parameters, and mechanical properties of peanut pods, pod stalks, and stems were measured through physical experiments. The Hertz–Mindlin model was used for non-bonded contacts, whereas the Hertz–Mindlin with Bonding model was adopted to represent the flexible connections among plant organs and the fracture behaviors of the pod–pod stalk junction. The main DEM parameters were calibrated using Plackett–Burman screening, steepest ascent experiments, and central composite design. The results showed that the tangential stiffness per unit area and tangential critical stress at the pod–pod stalk junction were the dominant factors affecting pod detachment force. The optimized parameter combination was a tangential stiffness per unit area of 4.738 × 10⁵ N/m³ and a tangential critical stress of 9.350 × 10⁵ Pa, corresponding to a simulated tensile force of 6.73 N. Model validation was performed by comparing peanut harvesting simulations with field trials. The relative error of pod loss rate between simulation and field measurement was less than 7.55%, and the t-test result indicated no significant difference between the two datasets (p > 0.05). These results demonstrate that the proposed flexible peanut plant model can effectively characterize pod–pod stalk separation and can provide a reliable DEM modelling basis for peanut harvesting process analysis and equipment optimization. Full article

The Yellow River Delta (YRD), a crucial ecotone, is becoming increasingly polluted by antibiotics, posing serious threats to aquatic ecosystems and human health. In this study, comprehensive investigations were conducted to explore the regional distribution, environmental risks, and source apportionment of antibiotics, with the aim of facilitating precise management and control of antibiotic pollution. The results show that the surge in runoff during the water–sediment regulation period (June and August) of the Yellow River drove a sharp rise in antibiotic concentrations in the surface water, peaking at 135.0 ng/L, whereas antibiotics were rarely detected in the sediments after multiple rounds of intense hydraulic scouring (0.2~12.6 ng/g in October). Furthermore, seven antibiotics (sulfadiazine, sulfamethoxazole, flumequine, ofloxacin, tetracycline, doxycycline, and lincomycin) in surface water and six antibiotics (norfloxacin, enrofloxacin, ofloxacin, doxycycline, oxytetracycline, and florfenicol) in sediments were identified as representative compounds according to the antibiotic pollution profiles. Environmental risk assessment coupled with spatial autocorrelation analysis revealed that sulfamethoxazole generally posed medium to high risk (0.12~1.27) in surface water. Sediments posed more serious ecological risks, with universally high risk levels (ranging from 1.11 to 280.00). More importantly, in both surface water and sediment, four core antibiotic sources—namely, human sewage, livestock farming, agricultural and aquaculture inputs, and hydrodynamic-driven resuspension processes—were consistently identified through the Positive Matrix Factorization model and Kriging interpolation. These findings provide crucial insights for establishing targeted antibiotic pollution control strategies in the YRD and advance the understanding of antibiotic fate in sediment-laden rivers. Full article

Tuberculosis (TB) remains a major public health burden in China. Although meteorological and environmental factors are recognized to influence TB transmission, their non-linear effects and spatiotemporal heterogeneity have not been fully elucidated. Based on monthly TB incidence data from 31 provinces in China during 2005–2020, this study systematically investigated these effects by integrating nine meteorological and air pollution variables within a combined machine learning and spatial statistical modeling framework. The results indicated that the Extreme Gradient Boosting (XGBoost) model effectively captured the complex non-linear relationships between environmental exposure and TB incidence. SHAP interpretability analysis identified surface pressure (SP), vegetation coverage, and PM_2.5 as the key drivers and revealed pronounced nonlinear response patterns and threshold effects. In particular, the promoting effect of PM_2.5 on TB incidence increased sharply at medium-to-high concentration levels. To further investigate spatial and temporal non-stationarity, Geographically and Temporally Weighted Regression (GTWR) was applied. The results demonstrated strong spatiotemporal heterogeneity in driver effects across provinces. The influence of PM_2.5 showed a consistently positive association with TB incidence and exhibited a distinct temporal evolution characterized by an initial strengthening before 2015 followed by a weakening thereafter, closely aligning with China’s air pollution control process. These findings provide new insights into the nonlinear and spatiotemporally heterogeneous effects of meteorological and environmental factors on TB incidence and support the development of more targeted, region-specific TB prevention strategies. Full article